Machines performing aggregated operations also imply that different process steps need to interact properly. As such machines become more complex, it may be hard for an operator to have a clear overview of the complex machinery. One issue that may occur due to this is that the operator may not be able to distinguish between errors, such as a part or unit of the machine is malfunctioning because something is broken, stuck or jammed, and normal events that may temporary delay, stall or reduce capacity of the machine, which may be caused by normal physical limitations such as the time needed for heating or cooling a batch of material to be used.
The complex machines can be monitored by using a multitude of sensors or collecting information from a multitude of local controllers in the different units of the machine. The information from these sensors and/or controllers may constitute a large amount of data. Traditionally, these data have been presented or been able to be accessed by the operator, and normally only as a code and/or values for the respective signal, and sometimes where the code is translated to a short description for respective signal. Further, alarm notification approaches have been used for actual errors, wherein the alarm mechanism may have been used such that alarms also are generated where there actually is no real error. This may give an operator a misleading picture of the state of the machine.
An operator may have problems interpreting the situation from this large amount of data, and when the machine for example stalls or slows down, the operator may misinterpret the data for being an error, stop the machine and call for service, although the machine only was temporarily waiting or slowing down due to natural causes.
It is therefore a desire to provide an approach for facilitating for the operator to interpret the status of the machine with the aim to improve machine operation and thus efficiency of the machine.